#take averages
cost10.B0.avg <- trait.avg(s1.new.B0.cost10[[1]])
cost10.B.1.avg <- trait.avg(s1.new.B.1.cost10[[1]])
cost10.B.2.avg <- trait.avg(s1.new.B.2.cost10[[1]])
cost10.B.3.avg <- trait.avg(s1.new.B.3.cost10[[1]])
cost10.B.4.avg <- trait.avg(s1.new.B.4.cost10[[1]])
cost10.B.5.avg <- trait.avg(s1.new.B.5.cost10[[1]])
cost10.B.6.avg <- trait.avg(s1.new.B.6.cost10[[1]])
cost10.B.7.avg <- trait.avg(s1.new.B.7.cost10[[1]])
cost10.B.8.avg <- trait.avg(s1.new.B.8.cost10[[1]])
cost10.B.9.avg <- trait.avg(s1.new.B.9.cost10[[1]])
cost10.B1.avg <- trait.avg(s1.new.B1.cost10[[1]])
#group
cost10.averages <- rbind(cost10.B0.avg, cost10.B.1.avg, cost10.B.2.avg, cost10.B.3.avg, cost10.B.4.avg, cost10.B.5.avg, cost10.B.6.avg, cost10.B.7.avg, cost10.B.8.avg, cost10.B.9.avg, cost10.B1.avg)
cost10.averages$B <- NA
cost10.averages$B <- rep(seq(0,1,0.1), each=2000)
library(ggplot2)
ggplot(data=subset(cost10.averages, generation==200), aes(x=B,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(cost10.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")

cost10.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost10[[2]], s1.new.B.1.cost10[[2]], s1.new.B.2.cost10[[2]], s1.new.B.3.cost10[[2]], s1.new.B.4.cost10[[2]], s1.new.B.5.cost10[[2]], s1.new.B.6.cost10[[2]], s1.new.B.7.cost10[[2]], s1.new.B.8.cost10[[2]], s1.new.B.9.cost10[[2]], s1.new.B1.cost10[[2]])
cost10.B.drift.outcomes$B <- NA
cost10.B.drift.outcomes$B[1:5000] <- "drift"
cost10.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost10.B.drift.outcomes$B <- factor(cost10.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost10.B.drift.outcomes$green.outcome <- NA
cost10.B.drift.outcomes$green.outcome[cost10.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost10.B.drift.outcomes$green.outcome[cost10.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost10.model <- glm(green.outcome~B, data=cost10.B.drift.outcomes, family="binomial")
summary(cost10.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost10.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5764 -1.1603 0.8254 1.1946 1.5317
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.040397 0.028864 -1.400 0.16165
B0 0.942299 0.115693 8.145 3.80e-16 ***
B0.1 0.759911 0.110382 6.884 5.80e-12 ***
B0.2 0.771284 0.110586 6.975 3.07e-12 ***
B0.3 0.488946 0.105539 4.633 3.61e-06 ***
B0.4 0.282783 0.105032 2.692 0.00710 **
B0.5 0.342678 0.105182 3.258 0.00112 **
B0.6 -0.004951 0.104470 -0.047 0.96220
B0.7 -0.232382 0.106089 -2.190 0.02849 *
B0.8 -0.308271 0.106288 -2.900 0.00373 **
B0.9 -0.762501 0.111433 -6.843 7.77e-12 ***
B1 -0.565129 0.105866 -5.338 9.39e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12759 on 9204 degrees of freedom
Residual deviance: 12437 on 9193 degrees of freedom
(1295 observations deleted due to missingness)
AIC: 12461
Number of Fisher Scoring iterations: 4
plot_model(cost10.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

#determining likelihood of any trait going into fixation by generation 200
cost10.B.drift.outcomes$any.outcome <- NA
cost10.B.drift.outcomes$any.outcome[is.na(cost10.B.drift.outcomes$e)] <- FALSE
cost10.B.drift.outcomes$any.outcome[!is.na(cost10.B.drift.outcomes$e)] <- TRUE
#create model and plot
cost10.model.b <- glm(any.outcome~B, data=cost10.B.drift.outcomes, family="binomial")
summary(cost10.model.b)
Call:
glm(formula = any.outcome ~ B, family = "binomial", data = cost10.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5432 0.2835 0.2835 0.6681 0.7122
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.19379 0.07269 43.94 <2e-16 ***
B0 -1.95129 0.12958 -15.06 <2e-16 ***
B0.1 -1.80750 0.13336 -13.55 <2e-16 ***
B0.2 -1.80750 0.13336 -13.55 <2e-16 ***
B0.3 -1.70431 0.13640 -12.49 <2e-16 ***
B0.4 -1.83231 0.13267 -13.81 <2e-16 ***
B0.5 -1.80750 0.13336 -13.55 <2e-16 ***
B0.6 -1.84458 0.13233 -13.94 <2e-16 ***
B0.7 -1.91643 0.13045 -14.69 <2e-16 ***
B0.8 -1.88088 0.13136 -14.32 <2e-16 ***
B0.9 -1.75673 0.13482 -13.03 <2e-16 ***
B1 -1.50550 0.14309 -10.52 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7843.8 on 10499 degrees of freedom
Residual deviance: 7148.4 on 10488 degrees of freedom
AIC: 7172.4
Number of Fisher Scoring iterations: 6
plot_model(cost10.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
Profiled confidence intervals may take longer time to compute.
Use `ci_method="wald"` for faster computation of CIs.

#B.25
cost0.B.25.avg <- trait.avg(s1.new.B.25.cost0[[1]])
cost10.B.25.avg <- trait.avg(s1.new.B.25.cost10[[1]])
cost20.B.25.avg <- trait.avg(s1.new.B.25.cost20[[1]])
cost30.B.25.avg <- trait.avg(s1.new.B.25.cost30[[1]])
cost40.B.25.avg <- trait.avg(s1.new.B.25.cost40[[1]])
cost50.B.25.avg <- trait.avg(s1.new.B.25.cost50[[1]])
cost60.B.25.avg <- trait.avg(s1.new.B.25.cost60[[1]])
cost70.B.25.avg <- trait.avg(s1.new.B.25.cost70[[1]])
cost80.B.25.avg <- trait.avg(s1.new.B.25.cost80[[1]])
cost90.B.25.avg <- trait.avg(s1.new.B.25.cost90[[1]])
cost100.B.25.avg <- trait.avg(s1.new.B.25.cost100[[1]])
#group
B.25.averages <- rbind(cost0.B.25.avg, cost10.B.25.avg, cost20.B.25.avg, cost30.B.25.avg, cost40.B.25.avg, cost50.B.25.avg, cost60.B.25.avg, cost70.B.25.avg, cost80.B.25.avg, cost90.B.25.avg, cost100.B.25.avg)
B.25.averages$cost <- NA
B.25.averages$cost <- rep(seq(0,100,10), each=2000)
library(ggplot2)
ggplot(data=subset(B.25.averages, generation==200), aes(x=cost,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(B.25.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")

B.25.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.25.cost0[[2]], s1.new.B.25.cost10[[2]], s1.new.B.25.cost20[[2]], s1.new.B.25.cost30[[2]], s1.new.B.25.cost40[[2]], s1.new.B.25.cost50[[2]], s1.new.B.25.cost60[[2]], s1.new.B.25.cost70[[2]], s1.new.B.25.cost80[[2]], s1.new.B.25.cost90[[2]], s1.new.B.25.cost100[[2]])
B.25.cost.drift.outcomes$cost <- NA
B.25.cost.drift.outcomes$cost[1:5000] <- "drift"
B.25.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.25.cost.drift.outcomes$cost <- factor(B.25.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
library(colorRamps)
B.25.cost.drift.outcomes$green.outcome <- NA
B.25.cost.drift.outcomes$green.outcome[B.25.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.25.cost.drift.outcomes$green.outcome[B.25.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
summary(B.25.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B.25.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5898 -1.1603 0.8383 1.1946 1.1946
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
cost0 0.33532 0.10477 3.201 0.00137 **
cost10 0.75274 0.11130 6.763 1.35e-11 ***
cost20 0.87331 0.11309 7.722 1.14e-14 ***
cost30 0.60587 0.10758 5.632 1.78e-08 ***
cost40 0.97196 0.11290 8.609 < 2e-16 ***
cost50 0.68635 0.10790 6.361 2.01e-10 ***
cost60 0.90539 0.11258 8.042 8.84e-16 ***
cost70 0.66807 0.10786 6.194 5.88e-10 ***
cost80 0.93899 0.11479 8.180 2.84e-16 ***
cost90 0.79641 0.10978 7.255 4.02e-13 ***
cost100 0.81483 0.11232 7.255 4.03e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12578 on 9230 degrees of freedom
Residual deviance: 12235 on 9219 degrees of freedom
(1269 observations deleted due to missingness)
AIC: 12259
Number of Fisher Scoring iterations: 4
plot_model(B.25.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

B.25.model.b <- glm(any.outcome~cost, data=B.25.cost.drift.outcomes, family="binomial")
summary(B.25.model.b)
Call:
glm(formula = any.outcome ~ cost, family = "binomial", data = B.25.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5432 0.2835 0.2835 0.6492 0.6976
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.19379 0.07269 43.94 <2e-16 ***
cost0 -1.76957 0.13444 -13.16 <2e-16 ***
cost10 -1.90466 0.13075 -14.57 <2e-16 ***
cost20 -1.85677 0.13200 -14.07 <2e-16 ***
cost30 -1.76957 0.13444 -13.16 <2e-16 ***
cost40 -1.62227 0.13902 -11.67 <2e-16 ***
cost50 -1.67744 0.13724 -12.22 <2e-16 ***
cost60 -1.74378 0.13520 -12.90 <2e-16 ***
cost70 -1.70431 0.13640 -12.49 <2e-16 ***
cost80 -1.88088 0.13136 -14.32 <2e-16 ***
cost90 -1.67744 0.13724 -12.22 <2e-16 ***
cost100 -1.89281 0.13105 -14.44 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7741.2 on 10499 degrees of freedom
Residual deviance: 7078.6 on 10488 degrees of freedom
AIC: 7102.6
Number of Fisher Scoring iterations: 6
plot_model(B.25.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
Profiled confidence intervals may take longer time to compute.
Use `ci_method="wald"` for faster computation of CIs.

#B.6
cost0.B.6.avg <- trait.avg(s1.new.B.6.cost0[[1]])
cost10.B.6.avg <- trait.avg(s1.new.B.6.cost10[[1]])
#group
B.6.averages <- rbind(cost0.B.6.avg, cost10.B.6.avg, cost20.B.6.avg, cost30.B.6.avg, cost40.B.6.avg, cost50.B.6.avg, cost60.B.6.avg, cost70.B.6.avg, cost80.B.6.avg, cost90.B.6.avg, cost100.B.6.avg)
B.6.averages$cost <- NA
B.6.averages$cost <- rep(seq(0,100,10), each=2000)
library(ggplot2)
ggplot(data=subset(B.6.averages, generation==200), aes(x=cost,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(B.6.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")

B.6.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.6.cost0[[2]], s1.new.B.6.cost10[[2]], s1.new.B.6.cost20[[2]], s1.new.B.6.cost30[[2]], s1.new.B.6.cost40[[2]], s1.new.B.6.cost50[[2]], s1.new.B.6.cost60[[2]], s1.new.B.6.cost70[[2]], s1.new.B.6.cost80[[2]], s1.new.B.6.cost90[[2]], s1.new.B.6.cost100[[2]])
B.6.cost.drift.outcomes$cost <- NA
B.6.cost.drift.outcomes$cost[1:5000] <- "drift"
B.6.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.6.cost.drift.outcomes$cost <- factor(B.6.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
library(colorRamps)
B.6.cost.drift.outcomes$green.outcome <- NA
B.6.cost.drift.outcomes$green.outcome[B.6.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.6.cost.drift.outcomes$green.outcome[B.6.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
B.6.model <- glm(green.outcome~cost, data=B.6.cost.drift.outcomes, family="binomial")
summary(B.6.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B.6.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.244 -1.160 -1.069 1.195 1.289
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.040397 0.028864 -1.400 0.1617
cost0 -0.219280 0.105504 -2.078 0.0377 *
cost10 -0.048771 0.103701 -0.470 0.6381
cost20 0.196099 0.104494 1.877 0.0606 .
cost30 -0.008874 0.103399 -0.086 0.9316
cost40 -0.061213 0.104936 -0.583 0.5597
cost50 -0.044020 0.103809 -0.424 0.6715
cost60 0.135231 0.104070 1.299 0.1938
cost70 -0.057242 0.103017 -0.556 0.5784
cost80 0.050153 0.102905 0.487 0.6260
cost90 -0.035976 0.105005 -0.343 0.7319
cost100 0.116000 0.104514 1.110 0.2670
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12771 on 9214 degrees of freedom
Residual deviance: 12758 on 9203 degrees of freedom
(1285 observations deleted due to missingness)
AIC: 12782
Number of Fisher Scoring iterations: 3
plot_model(B.6.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

#determining likelihood of any trait going into fixation by generation 200
B.6.cost.drift.outcomes$any.outcome <- NA
B.6.cost.drift.outcomes$any.outcome[is.na(B.6.cost.drift.outcomes$e)] <- FALSE
B.6.cost.drift.outcomes$any.outcome[!is.na(B.6.cost.drift.outcomes$e)] <- TRUE
#create model and plot
B.6.model.b <- glm(any.outcome~cost, data=B.6.cost.drift.outcomes, family="binomial")
summary(B.6.model.b)
Call:
glm(formula = any.outcome ~ cost, family = "binomial", data = B.6.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5432 0.2835 0.2835 0.6568 0.6940
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.19379 0.07269 43.94 <2e-16 ***
cost0 -1.86887 0.13168 -14.19 <2e-16 ***
cost10 -1.75673 0.13482 -13.03 <2e-16 ***
cost20 -1.81995 0.13301 -13.68 <2e-16 ***
cost30 -1.73073 0.13559 -12.76 <2e-16 ***
cost40 -1.88088 0.13136 -14.32 <2e-16 ***
cost50 -1.76957 0.13444 -13.16 <2e-16 ***
cost60 -1.79495 0.13371 -13.42 <2e-16 ***
cost70 -1.67744 0.13724 -12.22 <2e-16 ***
cost80 -1.67744 0.13724 -12.22 <2e-16 ***
cost90 -1.89281 0.13105 -14.44 <2e-16 ***
cost100 -1.84458 0.13233 -13.94 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 7804.5 on 10499 degrees of freedom
Residual deviance: 7126.7 on 10488 degrees of freedom
AIC: 7150.7
Number of Fisher Scoring iterations: 6
plot_model(B.6.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
Profiled confidence intervals may take longer time to compute.
Use `ci_method="wald"` for faster computation of CIs.

#extract odds ratios and confidence intervals from 5 models — varying B and holding cost at 10
B.model1.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(cost10.model$coefficients)[-1], low = exp(confint(cost10.model))[-1,1], high = exp(confint(cost10.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model2.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s2.cost10.model$coefficients)[-1], low = exp(confint(s2.cost10.model))[-1,1], high = exp(confint(s2.cost10.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model3.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s3.cost.model$coefficients)[-1], low = exp(confint(s3.cost.model))[-1,1], high = exp(confint(s3.cost.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model4.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s4.cost.model$coefficients)[-1], low = exp(confint(s4.cost.model))[-1,1], high = exp(confint(s4.cost.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model5.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s5.cost.model$coefficients)[-1], low = exp(confint(s5.cost.model))[-1,1], high = exp(confint(s5.cost.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model.outputs <- rbind(B.model1.outputs, B.model2.outputs, B.model3.outputs, B.model4.outputs, B.model5.outputs)
ggplot(data = B.model.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 111)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, 0.1))

ggplot(data = B.model.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 111)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, 0.1))

ggplot(data = B.model.outputs[-c(which(B.model.outputs$B == 0), which(B.model.outputs$B == 0.1), which(B.model.outputs$B == 0.2), which(B.model.outputs$B == 0.3)),], aes(x = as.numeric(as.character(B)), y = OR, color = model, fill = model))+
geom_line()+
geom_point(size = 1.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0.4, 1, 0.1))

ggplot(data = B.model.outputs[c(which(B.model.outputs$B == 0), which(B.model.outputs$B == 0.1), which(B.model.outputs$B == 0.2), which(B.model.outputs$B == 0.3)),], aes(x = as.numeric(as.character(B)), y = OR, color = model, fill = model))+
geom_line()+
geom_point(size = 1.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
ylim(0,10)+
scale_x_continuous(breaks = seq(0, 0.4, 0.1))

#extract odds ratios and confidence intervals from 5 models — varying cost and holding B at 0.6 (0.7 and 0.8 in models 2 and 5, respectively)
cost.model1.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(B.6.model$coefficients)[-1], low = exp(confint(B.6.model))[-1,1], high = exp(confint(B.6.model))[-1,2])
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cost.model2.outputs <- data.frame(model = rep(NA, 11), B = rep(0.7,11), cost = seq(0,100,10), OR = exp(s2.B.7.model$coefficients)[-1], low = exp(confint(s2.B.7.model))[-1,1], high = exp(confint(s2.B.7.model))[-1,2])
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cost.model3.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(s3.B.6.model$coefficients)[-1], low = exp(confint(s3.B.6.model))[-1,1], high = exp(confint(s3.B.6.model))[-1,2])
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cost.model4.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(s4.B.6.model$coefficients)[-1], low = exp(confint(s4.B.6.model))[-1,1], high = exp(confint(s4.B.6.model))[-1,2])
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cost.model5.outputs <- data.frame(model = rep(NA, 11), B = rep(0.8,11), cost = seq(0,100,10), OR = exp(s5.B.8.model$coefficients)[-1], low = exp(confint(s5.B.8.model))[-1,1], high = exp(confint(s5.B.8.model))[-1,2])
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cost.model.outputs <- rbind(cost.model1.outputs, cost.model2.outputs, cost.model3.outputs, cost.model4.outputs, cost.model5.outputs)
cost.model.outputs$model[1:11] <- "Model 1"
cost.model.outputs$model[12:22] <- "Model 2"
cost.model.outputs$model[23:33] <- "Model 3"
cost.model.outputs$model[34:44] <- "Model 4"
cost.model.outputs$model[45:55] <- "Model 5"
cost.model.outputs$model <- factor(B.model.outputs$model)
ggplot(data = cost.model.outputs, aes(x = as.numeric(as.character(cost)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 2.5)+
ylab("Odds ratios for a green outcome")+
xlab("Value of cost")+
scale_x_continuous(breaks = seq(0, 100, 10))

B0.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost0[[2]], s1.new.B0.cost10[[2]], s1.new.B0.cost20[[2]], s1.new.B0.cost30[[2]], s1.new.B0.cost40[[2]], s1.new.B0.cost50[[2]], s1.new.B0.cost60[[2]], s1.new.B0.cost70[[2]], s1.new.B0.cost80[[2]], s1.new.B0.cost90[[2]], s1.new.B0.cost100[[2]])
B0.cost.drift.outcomes$cost <- NA
B0.cost.drift.outcomes$cost[1:5000] <- "drift"
B0.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B0.cost.drift.outcomes$cost <- factor(B0.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
library(colorRamps)
B0.cost.drift.outcomes$green.outcome <- NA
B0.cost.drift.outcomes$green.outcome[B0.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B0.cost.drift.outcomes$green.outcome[B0.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
B0.model <- glm(green.outcome~cost, data=B0.cost.drift.outcomes, family="binomial")
summary(B0.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B0.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6515 -1.1603 0.8024 1.1946 1.1946
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.162
cost0 0.13055 0.09930 1.315 0.189
cost10 0.96368 0.11441 8.423 < 2e-16 ***
cost20 0.97704 0.11765 8.305 < 2e-16 ***
cost30 0.89809 0.11141 8.061 7.54e-16 ***
cost40 1.00853 0.11615 8.683 < 2e-16 ***
cost50 1.10886 0.11889 9.327 < 2e-16 ***
cost60 0.87918 0.11389 7.720 1.17e-14 ***
cost70 0.92423 0.11273 8.198 2.44e-16 ***
cost80 0.93737 0.11435 8.198 2.45e-16 ***
cost90 1.02796 0.11488 8.948 < 2e-16 ***
cost100 0.86202 0.11155 7.728 1.10e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12521 on 9241 degrees of freedom
Residual deviance: 12046 on 9230 degrees of freedom
(1258 observations deleted due to missingness)
AIC: 12070
Number of Fisher Scoring iterations: 4
plot_model(B0.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

B.75.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.75.cost0[[2]], s1.new.B.75.cost10[[2]], s1.new.B.75.cost20[[2]], s1.new.B.75.cost30[[2]], s1.new.B.75.cost40[[2]], s1.new.B.75.cost50[[2]], s1.new.B.75.cost60[[2]], s1.new.B.75.cost70[[2]], s1.new.B.75.cost80[[2]], s1.new.B.75.cost90[[2]], s1.new.B.75.cost100[[2]])
B.75.cost.drift.outcomes$cost <- NA
B.75.cost.drift.outcomes$cost[1:5000] <- "drift"
B.75.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.75.cost.drift.outcomes$cost <- factor(B.75.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
library(colorRamps)
B.75.cost.drift.outcomes$green.outcome <- NA
B.75.cost.drift.outcomes$green.outcome[B.75.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.75.cost.drift.outcomes$green.outcome[B.75.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
B.75.model <- glm(green.outcome~cost, data=B.75.cost.drift.outcomes, family="binomial")
summary(B.75.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B.75.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.1795 -1.1603 -0.9685 1.1946 1.4357
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
cost0 -0.54890 0.10682 -5.139 2.77e-07 ***
cost10 -0.19994 0.10564 -1.893 0.05840 .
cost20 -0.30294 0.10550 -2.871 0.00409 **
cost30 -0.32780 0.10559 -3.104 0.00191 **
cost40 -0.32305 0.10629 -3.039 0.00237 **
cost50 -0.43204 0.10665 -4.051 5.10e-05 ***
cost60 -0.32733 0.10620 -3.082 0.00206 **
cost70 -0.32968 0.10586 -3.114 0.00184 **
cost80 -0.45318 0.10690 -4.239 2.24e-05 ***
cost90 -0.47311 0.10752 -4.400 1.08e-05 ***
cost100 0.04538 0.10396 0.437 0.66244
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12664 on 9199 degrees of freedom
Residual deviance: 12577 on 9188 degrees of freedom
(1300 observations deleted due to missingness)
AIC: 12601
Number of Fisher Scoring iterations: 4
plot_model(B.75.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

B.75.model <- glm(green.outcome~cost, data=B.75.cost.drift.outcomes, family="binomial")
summary(B.75.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B.75.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.1795 -1.1603 -0.9685 1.1946 1.4357
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
cost0 -0.54890 0.10682 -5.139 2.77e-07 ***
cost10 -0.19994 0.10564 -1.893 0.05840 .
cost20 -0.30294 0.10550 -2.871 0.00409 **
cost30 -0.32780 0.10559 -3.104 0.00191 **
cost40 -0.32305 0.10629 -3.039 0.00237 **
cost50 -0.43204 0.10665 -4.051 5.10e-05 ***
cost60 -0.32733 0.10620 -3.082 0.00206 **
cost70 -0.32968 0.10586 -3.114 0.00184 **
cost80 -0.45318 0.10690 -4.239 2.24e-05 ***
cost90 -0.47311 0.10752 -4.400 1.08e-05 ***
cost100 0.04538 0.10396 0.437 0.66244
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12664 on 9199 degrees of freedom
Residual deviance: 12577 on 9188 degrees of freedom
(1300 observations deleted due to missingness)
AIC: 12601
Number of Fisher Scoring iterations: 4
plot_model(B.75.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

B1.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B1.cost0[[2]], s1.new.B1.cost10[[2]], s1.new.B1.cost20[[2]], s1.new.B1.cost30[[2]], s1.new.B1.cost40[[2]], s1.new.B1.cost50[[2]], s1.new.B1.cost60[[2]], s1.new.B1.cost70[[2]], s1.new.B1.cost80[[2]], s1.new.B1.cost90[[2]], s1.new.B1.cost100[[2]])
B1.cost.drift.outcomes$cost <- NA
B1.cost.drift.outcomes$cost[1:5000] <- "drift"
B1.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B1.cost.drift.outcomes$cost <- factor(B1.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
library(colorRamps)
B1.cost.drift.outcomes$green.outcome <- NA
B1.cost.drift.outcomes$green.outcome[B1.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B1.cost.drift.outcomes$green.outcome[B1.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
B1.model <- glm(green.outcome~cost, data=B1.cost.drift.outcomes, family="binomial")
summary(B1.model)
Call:
glm(formula = green.outcome ~ cost, family = "binomial", data = B1.cost.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.1603 -1.1603 -0.9225 1.1946 1.4791
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
cost0 -0.48750 0.10659 -4.574 4.80e-06 ***
cost10 -0.64553 0.10794 -5.981 2.22e-09 ***
cost20 -0.46136 0.10579 -4.361 1.29e-05 ***
cost30 -0.30791 0.10482 -2.938 0.00331 **
cost40 -0.53970 0.10766 -5.013 5.36e-07 ***
cost50 -0.51641 0.10542 -4.898 9.66e-07 ***
cost60 -0.59379 0.11187 -5.308 1.11e-07 ***
cost70 -0.48570 0.10503 -4.624 3.76e-06 ***
cost80 -0.32593 0.10533 -3.094 0.00197 **
cost90 -0.63061 0.10892 -5.790 7.06e-09 ***
cost100 -0.58401 0.10757 -5.429 5.67e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12688 on 9281 degrees of freedom
Residual deviance: 12532 on 9270 degrees of freedom
(1218 observations deleted due to missingness)
AIC: 12556
Number of Fisher Scoring iterations: 4
plot_model(B1.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

#model 1 B model outputs
cost.model1.B0.outputs <- data.frame(model = rep(NA, 11), B = rep(0,11), cost = seq(0,100,10), OR = exp(B0.model$coefficients)[-1], low = exp(confint(B0.model))[-1,1], high = exp(confint(B0.model))[-1,2])
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cost.model1.B.25.outputs <- data.frame(model = rep(NA, 11), B = rep(0.25,11), cost = seq(0,100,10), OR = exp(B.25.model$coefficients)[-1], low = exp(confint(B.25.model))[-1,1], high = exp(confint(B.25.model))[-1,2])
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cost.model1.B.6.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(B.6.model$coefficients)[-1], low = exp(confint(B.6.model))[-1,1], high = exp(confint(B.6.model))[-1,2])
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cost.model1.B.75.outputs <- data.frame(model = rep(NA, 11), B = rep(0.75,11), cost = seq(0,100,10), OR = exp(B.75.model$coefficients)[-1], low = exp(confint(B.75.model))[-1,1], high = exp(confint(B.75.model))[-1,2])
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cost.model1.B1.outputs <- data.frame(model = rep(NA, 11), B = rep(1,11), cost = seq(0,100,10), OR = exp(B1.model$coefficients)[-1], low = exp(confint(B1.model))[-1,1], high = exp(confint(B1.model))[-1,2])
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B.model1.outputs <- rbind(cost.model1.B0.outputs, cost.model1.B.25.outputs, cost.model1.B.6.outputs, cost.model1.B.75.outputs, cost.model1.B1.outputs)
B.model1.outputs$model[1:11] <- "B = 0"
B.model1.outputs$model[12:22] <- "B = .25"
B.model1.outputs$model[23:33] <- "B = .6"
B.model1.outputs$model[34:44] <- "B = .75"
B.model1.outputs$model[45:55] <- "B = 1"
B.model1.outputs$model <- factor(B.model1.outputs$model)
ggplot(data = B.model1.outputs, aes(x = as.numeric(as.character(cost)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 4)+
ylab("Odds ratios for a green outcome")+
xlab("Value of cost")+
scale_x_continuous(breaks = seq(0, 100, 10))+
theme(legend.title=element_blank())

cost0.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost0[[2]], s1.new.B.1.cost0[[2]], s1.new.B.2.cost0[[2]], s1.new.B.3.cost0[[2]], s1.new.B.4.cost0[[2]], s1.new.B.5.cost0[[2]], s1.new.B.6.cost0[[2]], s1.new.B.7.cost0[[2]], s1.new.B.8.cost0[[2]], s1.new.B.9.cost0[[2]], s1.new.B1.cost0[[2]])
cost0.B.drift.outcomes$B <- NA
cost0.B.drift.outcomes$B[1:5000] <- "drift"
cost0.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost0.B.drift.outcomes$B <- factor(cost0.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost0.B.drift.outcomes$green.outcome <- NA
cost0.B.drift.outcomes$green.outcome[cost0.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost0.B.drift.outcomes$green.outcome[cost0.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost0.model <- glm(green.outcome~B, data=cost0.B.drift.outcomes, family="binomial")
summary(cost0.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost0.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.3511 -1.1603 -0.9141 1.1946 1.4658
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
B0 0.13055 0.09930 1.315 0.18862
B0.1 0.33374 0.10369 3.219 0.00129 **
B0.2 0.43993 0.10353 4.249 2.14e-05 ***
B0.3 0.12166 0.10489 1.160 0.24610
B0.4 -0.20399 0.10444 -1.953 0.05080 .
B0.5 0.14368 0.10338 1.390 0.16457
B0.6 -0.21928 0.10551 -2.078 0.03767 *
B0.7 -0.47170 0.10497 -4.494 7.00e-06 ***
B0.8 -0.61612 0.10844 -5.682 1.33e-08 ***
B0.9 -0.56194 0.10761 -5.222 1.77e-07 ***
B1 -0.48750 0.10659 -4.574 4.80e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12897 on 9318 degrees of freedom
Residual deviance: 12746 on 9307 degrees of freedom
(1181 observations deleted due to missingness)
AIC: 12770
Number of Fisher Scoring iterations: 4
plot_model(cost0.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

cost25.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost25[[2]], s1.new.B.1.cost25[[2]], s1.new.B.2.cost25[[2]], s1.new.B.3.cost25[[2]], s1.new.B.4.cost25[[2]], s1.new.B.5.cost25[[2]], s1.new.B.6.cost25[[2]], s1.new.B.7.cost25[[2]], s1.new.B.8.cost25[[2]], s1.new.B.9.cost25[[2]], s1.new.B1.cost25[[2]])
cost25.B.drift.outcomes$B <- NA
cost25.B.drift.outcomes$B[1:5000] <- "drift"
cost25.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost25.B.drift.outcomes$B <- factor(cost25.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost25.B.drift.outcomes$green.outcome <- NA
cost25.B.drift.outcomes$green.outcome[cost25.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost25.B.drift.outcomes$green.outcome[cost25.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost25.model <- glm(green.outcome~B, data=cost25.B.drift.outcomes, family="binomial")
summary(cost25.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost25.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5981 -1.1603 0.8087 1.1946 1.3994
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.161651
B0 0.99035 0.11544 8.579 < 2e-16 ***
B0.1 0.90721 0.11435 7.933 2.13e-15 ***
B0.2 0.94091 0.11429 8.233 < 2e-16 ***
B0.3 0.74121 0.11110 6.672 2.53e-11 ***
B0.4 0.39114 0.10448 3.743 0.000181 ***
B0.5 0.23097 0.10452 2.210 0.027115 *
B0.6 0.18457 0.10397 1.775 0.075875 .
B0.7 -0.22959 0.10557 -2.175 0.029654 *
B0.8 -0.24444 0.10387 -2.353 0.018603 *
B0.9 -0.35891 0.10528 -3.409 0.000651 ***
B1 -0.46783 0.10599 -4.414 1.01e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12760 on 9215 degrees of freedom
Residual deviance: 12433 on 9204 degrees of freedom
(1284 observations deleted due to missingness)
AIC: 12457
Number of Fisher Scoring iterations: 4
plot_model(cost25.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

cost50.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost50[[2]], s1.new.B.1.cost50[[2]], s1.new.B.2.cost50[[2]], s1.new.B.3.cost50[[2]], s1.new.B.4.cost50[[2]], s1.new.B.5.cost50[[2]], s1.new.B.6.cost50[[2]], s1.new.B.7.cost50[[2]], s1.new.B.8.cost50[[2]], s1.new.B.9.cost50[[2]], s1.new.B1.cost50[[2]])
cost50.B.drift.outcomes$B <- NA
cost50.B.drift.outcomes$B[1:5000] <- "drift"
cost50.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost50.B.drift.outcomes$B <- factor(cost50.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost50.B.drift.outcomes$green.outcome <- NA
cost50.B.drift.outcomes$green.outcome[cost50.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost50.B.drift.outcomes$green.outcome[cost50.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost50.model <- glm(green.outcome~B, data=cost50.B.drift.outcomes, family="binomial")
summary(cost50.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost50.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6515 -1.1603 0.7685 1.1946 1.4211
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.161651
B0 1.10886 0.11889 9.327 < 2e-16 ***
B0.1 0.88648 0.11377 7.792 6.61e-15 ***
B0.2 0.66113 0.11018 6.001 1.96e-09 ***
B0.3 0.49359 0.10834 4.556 5.21e-06 ***
B0.4 0.25569 0.10452 2.446 0.014432 *
B0.5 0.36937 0.10407 3.549 0.000387 ***
B0.6 -0.04402 0.10381 -0.424 0.671529
B0.7 -0.33909 0.10784 -3.144 0.001664 **
B0.8 -0.09447 0.10418 -0.907 0.364516
B0.9 -0.34649 0.10552 -3.284 0.001025 **
B1 -0.51641 0.10542 -4.898 9.66e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12731 on 9188 degrees of freedom
Residual deviance: 12441 on 9177 degrees of freedom
(1311 observations deleted due to missingness)
AIC: 12465
Number of Fisher Scoring iterations: 4
plot_model(cost50.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

cost75.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost75[[2]], s1.new.B.1.cost75[[2]], s1.new.B.2.cost75[[2]], s1.new.B.3.cost75[[2]], s1.new.B.4.cost75[[2]], s1.new.B.5.cost75[[2]], s1.new.B.6.cost75[[2]], s1.new.B.7.cost75[[2]], s1.new.B.8.cost75[[2]], s1.new.B.9.cost75[[2]], s1.new.B1.cost75[[2]])
cost75.B.drift.outcomes$B <- NA
cost75.B.drift.outcomes$B[1:5000] <- "drift"
cost75.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost75.B.drift.outcomes$B <- factor(cost75.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost75.B.drift.outcomes$green.outcome <- NA
cost75.B.drift.outcomes$green.outcome[cost75.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost75.B.drift.outcomes$green.outcome[cost75.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost75.model <- glm(green.outcome~B, data=cost75.B.drift.outcomes, family="binomial")
summary(cost75.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost75.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6636 -1.1603 0.7597 1.1946 1.4404
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.161651
B0 1.13560 0.12021 9.447 < 2e-16 ***
B0.1 0.99522 0.11488 8.663 < 2e-16 ***
B0.2 0.91727 0.11284 8.129 4.33e-16 ***
B0.3 0.69744 0.10771 6.475 9.48e-11 ***
B0.4 0.27796 0.10404 2.672 0.007549 **
B0.5 0.20960 0.10553 1.986 0.047019 *
B0.6 0.04040 0.10384 0.389 0.697262
B0.7 -0.02180 0.10586 -0.206 0.836846
B0.8 -0.30617 0.10457 -2.928 0.003412 **
B0.9 -0.55949 0.10697 -5.231 1.69e-07 ***
B1 -0.41280 0.10834 -3.810 0.000139 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12748 on 9205 degrees of freedom
Residual deviance: 12387 on 9194 degrees of freedom
(1294 observations deleted due to missingness)
AIC: 12411
Number of Fisher Scoring iterations: 4
plot_model(cost75.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

cost100.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost100[[2]], s1.new.B.1.cost100[[2]], s1.new.B.2.cost100[[2]], s1.new.B.3.cost100[[2]], s1.new.B.4.cost100[[2]], s1.new.B.5.cost100[[2]], s1.new.B.6.cost100[[2]], s1.new.B.7.cost100[[2]], s1.new.B.8.cost100[[2]], s1.new.B.9.cost100[[2]], s1.new.B1.cost100[[2]])
cost100.B.drift.outcomes$B <- NA
cost100.B.drift.outcomes$B[1:5000] <- "drift"
cost100.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost100.B.drift.outcomes$B <- factor(cost100.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
library(colorRamps)
cost100.B.drift.outcomes$green.outcome <- NA
cost100.B.drift.outcomes$green.outcome[cost100.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost100.B.drift.outcomes$green.outcome[cost100.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
library(sjPlot)
cost100.model <- glm(green.outcome~B, data=cost100.B.drift.outcomes, family="binomial")
summary(cost100.model)
Call:
glm(formula = green.outcome ~ B, family = "binomial", data = cost100.B.drift.outcomes)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5877 -1.1603 0.8166 1.1946 1.4336
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.04040 0.02886 -1.400 0.16165
B0 0.94810 0.11326 8.371 < 2e-16 ***
B0.1 0.96734 0.11531 8.389 < 2e-16 ***
B0.2 0.76707 0.10987 6.981 2.92e-12 ***
B0.3 0.57884 0.10673 5.423 5.84e-08 ***
B0.4 0.45007 0.10660 4.222 2.42e-05 ***
B0.5 0.37937 0.10415 3.642 0.00027 ***
B0.6 0.11600 0.10451 1.110 0.26704
B0.7 -0.14668 0.10624 -1.381 0.16740
B0.8 -0.33584 0.10603 -3.167 0.00154 **
B0.9 -0.27913 0.10483 -2.663 0.00775 **
B1 -0.54423 0.10899 -4.994 5.93e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 12743 on 9202 degrees of freedom
Residual deviance: 12437 on 9191 degrees of freedom
(1297 observations deleted due to missingness)
AIC: 12461
Number of Fisher Scoring iterations: 4
plot_model(cost100.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")

#model 1 cost model outputs
B.model1.cost0.outputs <- data.frame(model = rep(NA, 11), cost = rep(0,11), B = seq(0,1,0.1), OR = exp(cost0.model$coefficients)[-1], low = exp(confint(cost0.model))[-1,1], high = exp(confint(cost0.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model1.cost10.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(cost10.model$coefficients)[-1], low = exp(confint(cost10.model))[-1,1], high = exp(confint(cost10.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model1.cost25.outputs <- data.frame(model = rep(NA, 11), cost = rep(25,11), B = seq(0,1,0.1), OR = exp(cost25.model$coefficients)[-1], low = exp(confint(cost25.model))[-1,1], high = exp(confint(cost25.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model1.cost50.outputs <- data.frame(model = rep(NA, 11), cost = rep(50,11), B = seq(0,1,0.1), OR = exp(cost50.model$coefficients)[-1], low = exp(confint(cost50.model))[-1,1], high = exp(confint(cost50.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model1.cost75.outputs <- data.frame(model = rep(NA, 11), cost = rep(75,11), B = seq(0,1,0.1), OR = exp(cost75.model$coefficients)[-1], low = exp(confint(cost75.model))[-1,1], high = exp(confint(cost75.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
B.model1.cost100.outputs <- data.frame(model = rep(NA, 11), cost = rep(100,11), B = seq(0,1,0.1), OR = exp(cost100.model$coefficients)[-1], low = exp(confint(cost100.model))[-1,1], high = exp(confint(cost100.model))[-1,2])
Waiting for profiling to be done...
Waiting for profiling to be done...
cost.model1.outputs <- rbind(B.model1.cost0.outputs, B.model1.cost10.outputs, B.model1.cost25.outputs, B.model1.cost50.outputs, B.model1.cost75.outputs, B.model1.cost100.outputs)
cost.model1.outputs$model[1:11] <- "cost = 0"
cost.model1.outputs$model[12:22] <- "cost = 10"
cost.model1.outputs$model[23:33] <- "cost = 25"
cost.model1.outputs$model[34:44] <- "cost = 50"
cost.model1.outputs$model[45:55] <- "cost = 75"
cost.model1.outputs$model[56:66] <- "cost = 100"
cost.model1.outputs$model <- factor(cost.model1.outputs$model)
ggplot(data = cost.model1.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(6)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 4)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, .1))+
theme(legend.title=element_blank())

---
title: "Shibboleth model 1 analyses"
output: html_notebook
---

```{r}
#take averages

cost10.B0.avg <- trait.avg(s1.new.B0.cost10[[1]])
cost10.B.1.avg <- trait.avg(s1.new.B.1.cost10[[1]])
cost10.B.2.avg <- trait.avg(s1.new.B.2.cost10[[1]])
cost10.B.3.avg <- trait.avg(s1.new.B.3.cost10[[1]])
cost10.B.4.avg <- trait.avg(s1.new.B.4.cost10[[1]])
cost10.B.5.avg <- trait.avg(s1.new.B.5.cost10[[1]])
cost10.B.6.avg <- trait.avg(s1.new.B.6.cost10[[1]])
cost10.B.7.avg <- trait.avg(s1.new.B.7.cost10[[1]])
cost10.B.8.avg <- trait.avg(s1.new.B.8.cost10[[1]])
cost10.B.9.avg <- trait.avg(s1.new.B.9.cost10[[1]])
cost10.B1.avg <- trait.avg(s1.new.B1.cost10[[1]])
```


```{r}
#group 

cost10.averages <- rbind(cost10.B0.avg, cost10.B.1.avg, cost10.B.2.avg, cost10.B.3.avg, cost10.B.4.avg, cost10.B.5.avg, cost10.B.6.avg, cost10.B.7.avg, cost10.B.8.avg, cost10.B.9.avg, cost10.B1.avg)
```

```{r}
cost10.averages$B <- NA
cost10.averages$B <- rep(seq(0,1,0.1), each=2000)
```

```{r}
library(ggplot2)
ggplot(data=subset(cost10.averages, generation==200), aes(x=B,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(cost10.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")
```

```{r}
cost10.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost10[[2]], s1.new.B.1.cost10[[2]], s1.new.B.2.cost10[[2]], s1.new.B.3.cost10[[2]], s1.new.B.4.cost10[[2]], s1.new.B.5.cost10[[2]], s1.new.B.6.cost10[[2]], s1.new.B.7.cost10[[2]], s1.new.B.8.cost10[[2]], s1.new.B.9.cost10[[2]], s1.new.B1.cost10[[2]])
```

```{r}
cost10.B.drift.outcomes$B <- NA
cost10.B.drift.outcomes$B[1:5000] <- "drift"
cost10.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost10.B.drift.outcomes$B <- factor(cost10.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost10.B.drift.outcomes$green.outcome <- NA
cost10.B.drift.outcomes$green.outcome[cost10.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost10.B.drift.outcomes$green.outcome[cost10.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost10.model <- glm(green.outcome~B, data=cost10.B.drift.outcomes, family="binomial")
summary(cost10.model)
```

```{r}
plot_model(cost10.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
#determining likelihood of any trait going into fixation by generation 200

cost10.B.drift.outcomes$any.outcome <- NA
cost10.B.drift.outcomes$any.outcome[is.na(cost10.B.drift.outcomes$e)] <- FALSE
cost10.B.drift.outcomes$any.outcome[!is.na(cost10.B.drift.outcomes$e)] <- TRUE
```

```{r}
#create model and plot

cost10.model.b <- glm(any.outcome~B, data=cost10.B.drift.outcomes, family="binomial")
summary(cost10.model.b)
plot_model(cost10.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
```
```{r}
#B.25

cost0.B.25.avg <- trait.avg(s1.new.B.25.cost0[[1]])
cost10.B.25.avg <- trait.avg(s1.new.B.25.cost10[[1]])
cost20.B.25.avg <- trait.avg(s1.new.B.25.cost20[[1]])
cost30.B.25.avg <- trait.avg(s1.new.B.25.cost30[[1]])
cost40.B.25.avg <- trait.avg(s1.new.B.25.cost40[[1]])
cost50.B.25.avg <- trait.avg(s1.new.B.25.cost50[[1]])
cost60.B.25.avg <- trait.avg(s1.new.B.25.cost60[[1]])
cost70.B.25.avg <- trait.avg(s1.new.B.25.cost70[[1]])
cost80.B.25.avg <- trait.avg(s1.new.B.25.cost80[[1]])
cost90.B.25.avg <- trait.avg(s1.new.B.25.cost90[[1]])
cost100.B.25.avg <- trait.avg(s1.new.B.25.cost100[[1]])
```

```{r}
#group 

B.25.averages <- rbind(cost0.B.25.avg, cost10.B.25.avg, cost20.B.25.avg, cost30.B.25.avg, cost40.B.25.avg, cost50.B.25.avg, cost60.B.25.avg, cost70.B.25.avg, cost80.B.25.avg, cost90.B.25.avg, cost100.B.25.avg)
```

```{r}
B.25.averages$cost <- NA
B.25.averages$cost <- rep(seq(0,100,10), each=2000)
```

```{r}
library(ggplot2)
ggplot(data=subset(B.25.averages, generation==200), aes(x=cost,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(B.25.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")
```
```{r}
B.25.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.25.cost0[[2]], s1.new.B.25.cost10[[2]], s1.new.B.25.cost20[[2]], s1.new.B.25.cost30[[2]], s1.new.B.25.cost40[[2]], s1.new.B.25.cost50[[2]], s1.new.B.25.cost60[[2]], s1.new.B.25.cost70[[2]], s1.new.B.25.cost80[[2]], s1.new.B.25.cost90[[2]], s1.new.B.25.cost100[[2]])
```

```{r}
B.25.cost.drift.outcomes$cost <- NA
B.25.cost.drift.outcomes$cost[1:5000] <- "drift"
B.25.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.25.cost.drift.outcomes$cost <- factor(B.25.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
```

```{r}
library(colorRamps)
B.25.cost.drift.outcomes$green.outcome <- NA
B.25.cost.drift.outcomes$green.outcome[B.25.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.25.cost.drift.outcomes$green.outcome[B.25.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
B.25.model <- glm(green.outcome~cost, data=B.25.cost.drift.outcomes, family="binomial")
summary(B.25.model)
plot_model(B.25.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```

```{r}
#determining likelihood of any trait going into fixation by generation 200

B.25.cost.drift.outcomes$any.outcome <- NA
B.25.cost.drift.outcomes$any.outcome[is.na(B.25.cost.drift.outcomes$e)] <- FALSE
B.25.cost.drift.outcomes$any.outcome[!is.na(B.25.cost.drift.outcomes$e)] <- TRUE

#create model and plot

B.25.model.b <- glm(any.outcome~cost, data=B.25.cost.drift.outcomes, family="binomial")
summary(B.25.model.b)
plot_model(B.25.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
```

```{r}
#B.6

cost0.B.6.avg <- trait.avg(s1.new.B.6.cost0[[1]])
cost10.B.6.avg <- trait.avg(s1.new.B.6.cost10[[1]])
cost20.B.6.avg <- trait.avg(s1.new.B.6.cost20[[1]])
cost30.B.6.avg <- trait.avg(s1.new.B.6.cost30[[1]])
cost40.B.6.avg <- trait.avg(s1.new.B.6.cost40[[1]])
cost50.B.6.avg <- trait.avg(s1.new.B.6.cost50[[1]])
cost60.B.6.avg <- trait.avg(s1.new.B.6.cost60[[1]])
cost70.B.6.avg <- trait.avg(s1.new.B.6.cost70[[1]])
cost80.B.6.avg <- trait.avg(s1.new.B.6.cost80[[1]])
cost90.B.6.avg <- trait.avg(s1.new.B.6.cost90[[1]])
cost100.B.6.avg <- trait.avg(s1.new.B.6.cost100[[1]])
```

```{r}
#group 

B.6.averages <- rbind(cost0.B.6.avg, cost10.B.6.avg, cost20.B.6.avg, cost30.B.6.avg, cost40.B.6.avg, cost50.B.6.avg, cost60.B.6.avg, cost70.B.6.avg, cost80.B.6.avg, cost90.B.6.avg, cost100.B.6.avg)
```

```{r}
B.6.averages$cost <- NA
B.6.averages$cost <- rep(seq(0,100,10), each=2000)
```

```{r}
library(ggplot2)
ggplot(data=subset(B.6.averages, generation==200), aes(x=cost,y=Freq, color=Var2))+
geom_line()+
scale_color_manual(values=unique(B.6.averages$Var2))+labs(y="frequency")+theme_bw()+theme(legend.position="none")
```

```{r}
B.6.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.6.cost0[[2]], s1.new.B.6.cost10[[2]], s1.new.B.6.cost20[[2]], s1.new.B.6.cost30[[2]], s1.new.B.6.cost40[[2]], s1.new.B.6.cost50[[2]], s1.new.B.6.cost60[[2]], s1.new.B.6.cost70[[2]], s1.new.B.6.cost80[[2]], s1.new.B.6.cost90[[2]], s1.new.B.6.cost100[[2]])
```

```{r}
B.6.cost.drift.outcomes$cost <- NA
B.6.cost.drift.outcomes$cost[1:5000] <- "drift"
B.6.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.6.cost.drift.outcomes$cost <- factor(B.6.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
```

```{r}
library(colorRamps)
B.6.cost.drift.outcomes$green.outcome <- NA
B.6.cost.drift.outcomes$green.outcome[B.6.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.6.cost.drift.outcomes$green.outcome[B.6.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
B.6.model <- glm(green.outcome~cost, data=B.6.cost.drift.outcomes, family="binomial")
summary(B.6.model)
plot_model(B.6.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```

```{r}
#determining likelihood of any trait going into fixation by generation 200

B.6.cost.drift.outcomes$any.outcome <- NA
B.6.cost.drift.outcomes$any.outcome[is.na(B.6.cost.drift.outcomes$e)] <- FALSE
B.6.cost.drift.outcomes$any.outcome[!is.na(B.6.cost.drift.outcomes$e)] <- TRUE

#create model and plot

B.6.model.b <- glm(any.outcome~cost, data=B.6.cost.drift.outcomes, family="binomial")
summary(B.6.model.b)
plot_model(B.6.model.b, show.values = TRUE, value.offset=.3, title="Odds ratios for any outcome")
```
```{r}
#extract odds ratios and confidence intervals from 5 models — varying B and holding cost at 10

B.model1.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(cost10.model$coefficients)[-1], low = exp(confint(cost10.model))[-1,1], high = exp(confint(cost10.model))[-1,2])

B.model2.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s2.cost10.model$coefficients)[-1], low = exp(confint(s2.cost10.model))[-1,1], high = exp(confint(s2.cost10.model))[-1,2])

B.model3.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s3.cost.model$coefficients)[-1], low = exp(confint(s3.cost.model))[-1,1], high = exp(confint(s3.cost.model))[-1,2])

B.model4.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s4.cost.model$coefficients)[-1], low = exp(confint(s4.cost.model))[-1,1], high = exp(confint(s4.cost.model))[-1,2])

B.model5.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(s5.cost.model$coefficients)[-1], low = exp(confint(s5.cost.model))[-1,1], high = exp(confint(s5.cost.model))[-1,2])

B.model.outputs <- rbind(B.model1.outputs, B.model2.outputs, B.model3.outputs, B.model4.outputs, B.model5.outputs)

B.model.outputs$model[1:11] <- "Model 1"
B.model.outputs$model[12:22] <- "Model 2"
B.model.outputs$model[23:33] <- "Model 3"
B.model.outputs$model[34:44] <- "Model 4"
B.model.outputs$model[45:55] <- "Model 5"
B.model.outputs$model <- factor(B.model.outputs$model)
```

```{r}
ggplot(data = B.model.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 111)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, 0.1)) 
```
```{r}
ggplot(data = B.model.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 111)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, 0.1)) 
```
```{r}
ggplot(data = B.model.outputs[-c(which(B.model.outputs$B == 0), which(B.model.outputs$B == 0.1), which(B.model.outputs$B == 0.2), which(B.model.outputs$B == 0.3)),], aes(x = as.numeric(as.character(B)), y = OR, color = model, fill = model))+
geom_line()+
geom_point(size = 1.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0.4, 1, 0.1)) 
```
```{r}
ggplot(data = B.model.outputs[c(which(B.model.outputs$B == 0), which(B.model.outputs$B == 0.1), which(B.model.outputs$B == 0.2), which(B.model.outputs$B == 0.3)),], aes(x = as.numeric(as.character(B)), y = OR, color = model, fill = model))+
geom_line()+
geom_point(size = 1.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
geom_ribbon(aes(y = OR, ymin = low, ymax = high), alpha = 0.2)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
ylim(0,10)+
scale_x_continuous(breaks = seq(0, 0.4, 0.1)) 
```
```{r}
#extract odds ratios and confidence intervals from 5 models — varying cost and holding B at 0.6 (0.7 and 0.8 in models 2 and 5, respectively)

cost.model1.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(B.6.model$coefficients)[-1], low = exp(confint(B.6.model))[-1,1], high = exp(confint(B.6.model))[-1,2])

cost.model2.outputs <- data.frame(model = rep(NA, 11), B = rep(0.7,11), cost = seq(0,100,10), OR = exp(s2.B.7.model$coefficients)[-1], low = exp(confint(s2.B.7.model))[-1,1], high = exp(confint(s2.B.7.model))[-1,2])

cost.model3.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(s3.B.6.model$coefficients)[-1], low = exp(confint(s3.B.6.model))[-1,1], high = exp(confint(s3.B.6.model))[-1,2])

cost.model4.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(s4.B.6.model$coefficients)[-1], low = exp(confint(s4.B.6.model))[-1,1], high = exp(confint(s4.B.6.model))[-1,2])

cost.model5.outputs <- data.frame(model = rep(NA, 11), B = rep(0.8,11), cost = seq(0,100,10), OR = exp(s5.B.8.model$coefficients)[-1], low = exp(confint(s5.B.8.model))[-1,1], high = exp(confint(s5.B.8.model))[-1,2])

cost.model.outputs <- rbind(cost.model1.outputs, cost.model2.outputs, cost.model3.outputs, cost.model4.outputs, cost.model5.outputs)

cost.model.outputs$model[1:11] <- "Model 1"
cost.model.outputs$model[12:22] <- "Model 2"
cost.model.outputs$model[23:33] <- "Model 3"
cost.model.outputs$model[34:44] <- "Model 4"
cost.model.outputs$model[45:55] <- "Model 5"
cost.model.outputs$model <- factor(B.model.outputs$model)
```
```{r}
ggplot(data = cost.model.outputs, aes(x = as.numeric(as.character(cost)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 2.5)+
ylab("Odds ratios for a green outcome")+
xlab("Value of cost")+
scale_x_continuous(breaks = seq(0, 100, 10)) 
```

```{r}
B0.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost0[[2]], s1.new.B0.cost10[[2]], s1.new.B0.cost20[[2]], s1.new.B0.cost30[[2]], s1.new.B0.cost40[[2]], s1.new.B0.cost50[[2]], s1.new.B0.cost60[[2]], s1.new.B0.cost70[[2]], s1.new.B0.cost80[[2]], s1.new.B0.cost90[[2]], s1.new.B0.cost100[[2]])
```

```{r}
B0.cost.drift.outcomes$cost <- NA
B0.cost.drift.outcomes$cost[1:5000] <- "drift"
B0.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B0.cost.drift.outcomes$cost <- factor(B0.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
```

```{r}
library(colorRamps)
B0.cost.drift.outcomes$green.outcome <- NA
B0.cost.drift.outcomes$green.outcome[B0.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B0.cost.drift.outcomes$green.outcome[B0.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
B0.model <- glm(green.outcome~cost, data=B0.cost.drift.outcomes, family="binomial")
summary(B0.model)
plot_model(B0.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
B.75.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B.75.cost0[[2]], s1.new.B.75.cost10[[2]], s1.new.B.75.cost20[[2]], s1.new.B.75.cost30[[2]], s1.new.B.75.cost40[[2]], s1.new.B.75.cost50[[2]], s1.new.B.75.cost60[[2]], s1.new.B.75.cost70[[2]], s1.new.B.75.cost80[[2]], s1.new.B.75.cost90[[2]], s1.new.B.75.cost100[[2]])
```

```{r}
B.75.cost.drift.outcomes$cost <- NA
B.75.cost.drift.outcomes$cost[1:5000] <- "drift"
B.75.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B.75.cost.drift.outcomes$cost <- factor(B.75.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
```

```{r}
library(colorRamps)
B.75.cost.drift.outcomes$green.outcome <- NA
B.75.cost.drift.outcomes$green.outcome[B.75.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B.75.cost.drift.outcomes$green.outcome[B.75.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
B.75.model <- glm(green.outcome~cost, data=B.75.cost.drift.outcomes, family="binomial")
summary(B.75.model)
plot_model(B.75.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```

```{r}
B1.cost.drift.outcomes <- rbind(drift5000[[2]], s1.new.B1.cost0[[2]], s1.new.B1.cost10[[2]], s1.new.B1.cost20[[2]], s1.new.B1.cost30[[2]], s1.new.B1.cost40[[2]], s1.new.B1.cost50[[2]], s1.new.B1.cost60[[2]], s1.new.B1.cost70[[2]], s1.new.B1.cost80[[2]], s1.new.B1.cost90[[2]], s1.new.B1.cost100[[2]])
```

```{r}
B1.cost.drift.outcomes$cost <- NA
B1.cost.drift.outcomes$cost[1:5000] <- "drift"
B1.cost.drift.outcomes$cost[5001:10500] <- rep(seq(0,100,10), each=500)
B1.cost.drift.outcomes$cost <- factor(B1.cost.drift.outcomes$cost, levels=c("drift","0","10","20","30","40","50","60","70","80","90","100"))
```

```{r}
library(colorRamps)
B1.cost.drift.outcomes$green.outcome <- NA
B1.cost.drift.outcomes$green.outcome[B1.cost.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
B1.cost.drift.outcomes$green.outcome[B1.cost.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
B1.model <- glm(green.outcome~cost, data=B1.cost.drift.outcomes, family="binomial")
summary(B1.model)
plot_model(B1.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
#model 1 B model outputs

cost.model1.B0.outputs <- data.frame(model = rep(NA, 11), B = rep(0,11), cost = seq(0,100,10), OR = exp(B0.model$coefficients)[-1], low = exp(confint(B0.model))[-1,1], high = exp(confint(B0.model))[-1,2])

cost.model1.B.25.outputs <- data.frame(model = rep(NA, 11), B = rep(0.25,11), cost = seq(0,100,10), OR = exp(B.25.model$coefficients)[-1], low = exp(confint(B.25.model))[-1,1], high = exp(confint(B.25.model))[-1,2])

cost.model1.B.6.outputs <- data.frame(model = rep(NA, 11), B = rep(0.6,11), cost = seq(0,100,10), OR = exp(B.6.model$coefficients)[-1], low = exp(confint(B.6.model))[-1,1], high = exp(confint(B.6.model))[-1,2])

cost.model1.B.75.outputs <- data.frame(model = rep(NA, 11), B = rep(0.75,11), cost = seq(0,100,10), OR = exp(B.75.model$coefficients)[-1], low = exp(confint(B.75.model))[-1,1], high = exp(confint(B.75.model))[-1,2])

cost.model1.B1.outputs <- data.frame(model = rep(NA, 11), B = rep(1,11), cost = seq(0,100,10), OR = exp(B1.model$coefficients)[-1], low = exp(confint(B1.model))[-1,1], high = exp(confint(B1.model))[-1,2])

B.model1.outputs <- rbind(cost.model1.B0.outputs, cost.model1.B.25.outputs, cost.model1.B.6.outputs, cost.model1.B.75.outputs, cost.model1.B1.outputs)

B.model1.outputs$model[1:11] <- "B = 0"
B.model1.outputs$model[12:22] <- "B = .25"
B.model1.outputs$model[23:33] <- "B = .6"
B.model1.outputs$model[34:44] <- "B = .75"
B.model1.outputs$model[45:55] <- "B = 1"
B.model1.outputs$model <- factor(B.model1.outputs$model, levels=c("B = 0", "B = .25", "B = .6", "B = .75", "B = 1"))
```

```{r}
ggplot(data = B.model1.outputs, aes(x = as.numeric(as.character(cost)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(5)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 4)+
ylab("Odds ratios for a green outcome")+
xlab("Value of cost")+
scale_x_continuous(breaks = seq(0, 100, 10))+
theme(legend.title=element_blank())
```
```{r}
cost0.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost0[[2]], s1.new.B.1.cost0[[2]], s1.new.B.2.cost0[[2]], s1.new.B.3.cost0[[2]], s1.new.B.4.cost0[[2]], s1.new.B.5.cost0[[2]], s1.new.B.6.cost0[[2]], s1.new.B.7.cost0[[2]], s1.new.B.8.cost0[[2]], s1.new.B.9.cost0[[2]], s1.new.B1.cost0[[2]])
```

```{r}
cost0.B.drift.outcomes$B <- NA
cost0.B.drift.outcomes$B[1:5000] <- "drift"
cost0.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost0.B.drift.outcomes$B <- factor(cost0.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost0.B.drift.outcomes$green.outcome <- NA
cost0.B.drift.outcomes$green.outcome[cost0.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost0.B.drift.outcomes$green.outcome[cost0.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost0.model <- glm(green.outcome~B, data=cost0.B.drift.outcomes, family="binomial")
summary(cost0.model)
plot_model(cost0.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```

```{r}
cost25.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost25[[2]], s1.new.B.1.cost25[[2]], s1.new.B.2.cost25[[2]], s1.new.B.3.cost25[[2]], s1.new.B.4.cost25[[2]], s1.new.B.5.cost25[[2]], s1.new.B.6.cost25[[2]], s1.new.B.7.cost25[[2]], s1.new.B.8.cost25[[2]], s1.new.B.9.cost25[[2]], s1.new.B1.cost25[[2]])
```

```{r}
cost25.B.drift.outcomes$B <- NA
cost25.B.drift.outcomes$B[1:5000] <- "drift"
cost25.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost25.B.drift.outcomes$B <- factor(cost25.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost25.B.drift.outcomes$green.outcome <- NA
cost25.B.drift.outcomes$green.outcome[cost25.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost25.B.drift.outcomes$green.outcome[cost25.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost25.model <- glm(green.outcome~B, data=cost25.B.drift.outcomes, family="binomial")
summary(cost25.model)
plot_model(cost25.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
cost50.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost50[[2]], s1.new.B.1.cost50[[2]], s1.new.B.2.cost50[[2]], s1.new.B.3.cost50[[2]], s1.new.B.4.cost50[[2]], s1.new.B.5.cost50[[2]], s1.new.B.6.cost50[[2]], s1.new.B.7.cost50[[2]], s1.new.B.8.cost50[[2]], s1.new.B.9.cost50[[2]], s1.new.B1.cost50[[2]])
```

```{r}
cost50.B.drift.outcomes$B <- NA
cost50.B.drift.outcomes$B[1:5000] <- "drift"
cost50.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost50.B.drift.outcomes$B <- factor(cost50.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost50.B.drift.outcomes$green.outcome <- NA
cost50.B.drift.outcomes$green.outcome[cost50.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost50.B.drift.outcomes$green.outcome[cost50.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost50.model <- glm(green.outcome~B, data=cost50.B.drift.outcomes, family="binomial")
summary(cost50.model)
plot_model(cost50.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
cost75.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost75[[2]], s1.new.B.1.cost75[[2]], s1.new.B.2.cost75[[2]], s1.new.B.3.cost75[[2]], s1.new.B.4.cost75[[2]], s1.new.B.5.cost75[[2]], s1.new.B.6.cost75[[2]], s1.new.B.7.cost75[[2]], s1.new.B.8.cost75[[2]], s1.new.B.9.cost75[[2]], s1.new.B1.cost75[[2]])
```

```{r}
cost75.B.drift.outcomes$B <- NA
cost75.B.drift.outcomes$B[1:5000] <- "drift"
cost75.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost75.B.drift.outcomes$B <- factor(cost75.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost75.B.drift.outcomes$green.outcome <- NA
cost75.B.drift.outcomes$green.outcome[cost75.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost75.B.drift.outcomes$green.outcome[cost75.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost75.model <- glm(green.outcome~B, data=cost75.B.drift.outcomes, family="binomial")
summary(cost75.model)
plot_model(cost75.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
cost100.B.drift.outcomes <- rbind(drift5000[[2]], s1.new.B0.cost100[[2]], s1.new.B.1.cost100[[2]], s1.new.B.2.cost100[[2]], s1.new.B.3.cost100[[2]], s1.new.B.4.cost100[[2]], s1.new.B.5.cost100[[2]], s1.new.B.6.cost100[[2]], s1.new.B.7.cost100[[2]], s1.new.B.8.cost100[[2]], s1.new.B.9.cost100[[2]], s1.new.B1.cost100[[2]])
```

```{r}
cost100.B.drift.outcomes$B <- NA
cost100.B.drift.outcomes$B[1:5000] <- "drift"
cost100.B.drift.outcomes$B[5001:10500] <- rep(seq(0,1,0.1), each=500)
cost100.B.drift.outcomes$B <- factor(cost100.B.drift.outcomes$B, levels=c("drift","0","0.1","0.2","0.3","0.4","0.5","0.6","0.7","0.8","0.9","1"))
```

```{r}
library(colorRamps)
cost100.B.drift.outcomes$green.outcome <- NA
cost100.B.drift.outcomes$green.outcome[cost100.B.drift.outcomes$e.var %in% blue2green(10)[1:5]] <- FALSE
cost100.B.drift.outcomes$green.outcome[cost100.B.drift.outcomes$e.var %in% blue2green(10)[6:10]] <- TRUE
```

```{r}
library(sjPlot)
cost100.model <- glm(green.outcome~B, data=cost100.B.drift.outcomes, family="binomial")
summary(cost100.model)
plot_model(cost100.model, show.values = TRUE, value.offset=.3, title="Odds ratios for green outcome")
```
```{r}
#model 1 cost model outputs

B.model1.cost0.outputs <- data.frame(model = rep(NA, 11), cost = rep(0,11), B = seq(0,1,0.1), OR = exp(cost0.model$coefficients)[-1], low = exp(confint(cost0.model))[-1,1], high = exp(confint(cost0.model))[-1,2])

B.model1.cost10.outputs <- data.frame(model = rep(NA, 11), cost = rep(10,11), B = seq(0,1,0.1), OR = exp(cost10.model$coefficients)[-1], low = exp(confint(cost10.model))[-1,1], high = exp(confint(cost10.model))[-1,2])

B.model1.cost25.outputs <- data.frame(model = rep(NA, 11), cost = rep(25,11), B = seq(0,1,0.1), OR = exp(cost25.model$coefficients)[-1], low = exp(confint(cost25.model))[-1,1], high = exp(confint(cost25.model))[-1,2])

B.model1.cost50.outputs <- data.frame(model = rep(NA, 11), cost = rep(50,11), B = seq(0,1,0.1), OR = exp(cost50.model$coefficients)[-1], low = exp(confint(cost50.model))[-1,1], high = exp(confint(cost50.model))[-1,2])

B.model1.cost75.outputs <- data.frame(model = rep(NA, 11), cost = rep(75,11), B = seq(0,1,0.1), OR = exp(cost75.model$coefficients)[-1], low = exp(confint(cost75.model))[-1,1], high = exp(confint(cost75.model))[-1,2])

B.model1.cost100.outputs <- data.frame(model = rep(NA, 11), cost = rep(100,11), B = seq(0,1,0.1), OR = exp(cost100.model$coefficients)[-1], low = exp(confint(cost100.model))[-1,1], high = exp(confint(cost100.model))[-1,2])

cost.model1.outputs <- rbind(B.model1.cost0.outputs, B.model1.cost10.outputs, B.model1.cost25.outputs, B.model1.cost50.outputs, B.model1.cost75.outputs, B.model1.cost100.outputs)

cost.model1.outputs$model[1:11] <- "cost = 0"
cost.model1.outputs$model[12:22] <- "cost = 10"
cost.model1.outputs$model[23:33] <- "cost = 25"
cost.model1.outputs$model[34:44] <- "cost = 50"
cost.model1.outputs$model[45:55] <- "cost = 75"
cost.model1.outputs$model[56:66] <- "cost = 100"


cost.model1.outputs$model <- factor(cost.model1.outputs$model, levels=c("cost = 0","cost = 10","cost = 25","cost = 50", "cost = 75","cost = 100"))
```

```{r}
ggplot(data = cost.model1.outputs, aes(x = as.numeric(as.character(B)), y = OR, color = model))+
geom_line()+
geom_point(size = 1)+
geom_ribbon(aes(y = OR, ymin = low, ymax = high, fill=model), alpha = 0.2)+
scale_color_manual(values=c(blue2green(6)))+
geom_hline(yintercept=1, linetype="dashed", color = "red")+
ylim(0, 4)+
ylab("Odds ratios for a green outcome")+
xlab("Value of B")+
scale_x_continuous(breaks = seq(0, 1, .1))+
theme(legend.title=element_blank())
```